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. Author manuscript; available in PMC: 2015 Sep 1.
Published in final edited form as: Ann Epidemiol. 2014 May 23;24(9):629–634.e1. doi: 10.1016/j.annepidem.2014.05.010

Mediators of the Association between Parental Severe Mental Illness and Offspring Neurodevelopmental Problems

Brittany M McCoy 1, Martin E Rickert 1, Quetzal A Class 1, Henrik Larsson 2, Paul Lichtenstein 2, Brian M D’Onofrio 1,3
PMCID: PMC4135008  NIHMSID: NIHMS601237  PMID: 25037304

Abstract

Purpose

Parental severe mental illness (SMI) is associated with an increased risk of offspring Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD). We conducted a study to examine the extent to which risk of preterm birth (PTB), low birth weight (LBW), and small for gestational age (SGA) mediated this association.

Methods

We obtained data on offspring born 1992-2001 in Sweden (n = 870,017) through the linkage of multiple population-based registers. We used logistic and Cox regression to assess the associations between parental SMI, adverse pregnancy outcomes, and offspring ASD and ADHD, as well as tested whether adverse pregnancy outcomes served as mediators.

Results

After controlling for measured covariates, maternal and paternal SMI were associated with an increased risk for PTB, LBW, and SGA, as well as for offspring ASD and ADHD. These pregnancy outcomes were also associated with an increased risk of ASD and ADHD. We found that pregnancy outcomes did not mediate the association between parental SMI and offspring ASD and ADHD, as there was no substantial change in magnitude of the risk estimates after controlling for pregnancy outcomes.

Conclusions

Parental SMI and adverse pregnancy outcomes appear to be independent risk factors for offspring ASD and ADHD.

Keywords: Attention Deficit Hyperactivity Disorder; Autism Spectrum Disorder; infant, small for gestational age; birth weight; gestational age


Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD) are neurodevelopmental problems whose symptoms often persist into and throughout adulthood, resulting in high societal costs and stress on families [1-5]. ASD encompasses three developmental disorders (i.e., Autism, Asperger’s, and Pervasive Developmental Disorder-Not Otherwise Specified) characterized by difficulties in communication and abnormalities in social interaction and behavior, whereas ADHD is described by inattention and hyperactivity [6, 7]. According to recent reports by the Centers for Disease Control and Prevention, the prevalence of ASD and ADHD are rising [6, 7]. Thus, research is needed to understand the etiology of both disorders.

One possible key to understanding the causal mechanisms of ASD and ADHD lies in the association between parental severe mental illness (SMI) and offspring neurodevelopmental problems [8-11]. Individuals with ADHD are at increased risk of having a first degree relative with schizophrenia or bipolar disorder [11]. These associations may be the result of shared genetic factors, as each disorder has been demonstrated to be highly heritable [12, 13]. Studies have also found that genetic factors are shared by numerous forms of severe psychopathology, suggesting that genetic factors typically influence multiple traits pleiotropically [12-14]. However, the current literature does not provide evidence for the causal mechanisms that underlie the association between parental SMI and offspring neurodevelopmental problems [8-11].

Adverse pregnancy outcomes such as preterm birth (PTB), low birth weight (LBW), and small for gestational age (SGA) are linked to both SMI and childhood neurodevelopmental problems [15-18]. This mutual association with adverse pregnancy outcomes may shed light on the mechanism linking parental SMI with offspring ASD and ADHD. Prescription drug use, alcohol use, and smoking during pregnancy have been cited as potential mechanisms that may explain the link between adult SMI and adverse birth outcomes in their offspring [15, 19]. It is hypothesized that the associations between PTB, LBW, and SGA and offspring ASD and ADHD arise from abnormalities in the developmental of nervous and endocrine systems resultant of restrictions to fetal growth in utero [20-28][29, 30]. Adverse pregnancy outcomes, thus, may serve as mediators in the association between parental SMI and offspring ASD and ADHD [31].

Few studies have examined adverse pregnancy outcomes as mediators in the relation between parental SMI and offspring neurodevelopmental problems, however. One previous study concluded that perinatal factors and parental psychiatric diagnoses were independent risk factors for ASD [10]. This study was limited by the researchers’ inability to analyze the independent association between maternal and paternal mental illness and offspring ASD. The results of such an analysis could provide further insights into whether the association between parental SMI and offspring neurodevelopmental problems may result from causal intrauterine effects [32]. And, the previous study only predicted ASD, while much can be gleaned out of additionally predicting ADHD, a condition highly related to ASD [33, 34].

We utilized prospectively-collected, population-based Swedish registers and logistic and Cox regression models to examine the extent to which adverse pregnancy outcomes act as mediators of the association between parental SMI and offspring ASD and ADHD. We hypothesized that adverse pregnancy outcomes would mediate, at least in part, the association between parental SMI and offspring ASD and ADHD.

METHODS

Study population

The study sample was obtained by linking information available in multiple Swedish population-based registers. Specifically, the Multi-Generation Register provides information on familial relationships in Sweden since 1933 [35]; the Medical Birth Registry contains data on more than 99% of births since 1973 [36]; the National Patient Register provides information on inpatient psychiatric diagnoses since 1973 and outpatient diagnoses since 2001; the Education Register provides information on the highest level of education completed; the Longitudinal Integration Database for Health Insurance and Social Studies (LISA) contains annual data since 1990 on income for individuals 15 years and older [37].

The initial cohort consisted of 980,046 offspring born in Sweden between 1992 and 2001. We excluded offspring who either died (4,255; 0.4%) or emigrated from Sweden (36,195; 3.7%). We also excluded offspring with a recorded gestational age under 23 weeks or over 42 weeks 6 days (7,228; 0.7%) in case gestational age was incorrectly recorded. Individuals born with congenital malformations (32,754; 3.3%) were then dropped, as were multiple births (26,210; 2.7%), given their increased risk of adverse pregnancy outcomes in comparison to singleton births [38]. Finally, we dropped individuals missing maternal (48; 0.005%) or paternal (3,339; 0.3%) identification numbers. The final sample of eligible Swedish births included data for 870,017 individuals born to 597,264 distinct mothers and 599,747 distinct fathers.

Measures

Maternal and paternal SMI

Cases of parental SMI were identified from well-validated inpatient data available in the National Patient Register [39]. Parents with a SMI were defined as those that had received a diagnosis of schizophrenia, bipolar disorder, or another non-organic psychosis according to ICD-8/9/10 criteria as a result of at least one hospital admission. Parents had to be at least 12 years old at the time of diagnosis. Parents with a SMI were included regardless of the timing of diagnosis in relation to childbirth. We explored SMI separately for both parents and constructed an index of parental SMI that included situations in which either or both parents had a diagnosis.

Offspring Neurodevelopmental Problems

Cases of offspring ASD and ADHD were identified using the National Patient Register [40] and defined as those that had received either an inpatient or outpatient diagnosis of ASD or ADHD according to ICD-9/10 criteria. Only individuals diagnosed before the age of 18 were included. We have documented the validity the diagnoses of ADHD [41], and the cases of ASD have been shown to be valid by our research group [42] and others [43].

Adverse pregnancy outcomes

Adverse pregnancy outcome data were obtained from the Medical Birth Registry. Birth weight was divided into five ordinal categories including less than 2,500g (LBW), 2,500-2,999g, 3,000-3,499g, 3,500-3,999g (reference group), and 4,000g and greater and an additional category for missing birth weight. Gestational age was divided into five categories of 23 weeks-27 weeks 6 days, 28 weeks-30 weeks 6 days, 31 weeks-33 weeks 6 days, 34 weeks-36 weeks 6 days, and 37 weeks-42 weeks 6 days (reference). An additional category of PTB was created as a combined measure of any birth before 37 weeks of gestation. The gestational age data recorded in the Medical Birth Registry is based on ultrasound estimates of gestational age during the second trimester and/or mother’s report of last menstruation at her first antenatal visit. Offspring born greater than two standard deviations below the average birth weight for a given gestational age were recorded in the Medical Birth Registry as being born SGA. We used a binary indicator of SGA status and included a missing category. These measures have been widely used in epidemiological studies and have been well validated [36].

Covariates

We controlled for offspring sex and coded parental country of origin as Sweden or not Sweden (reference category). Parental cohabitation status at birth was categorized as cohabitating (reference) or not cohabitating. We categorized parity as first-born (reference), second-born, third-born, and fourth-born or higher. Maternal and paternal ages at childbirth were separated into categories of under 21, 21 to 24, 25 to 29 (reference), 30 to 35, and above 35 years old. Parental criminality was a dichotomous variable indicating conviction of any crime at or after the age of 15 [44]. We categorized parental highest level of education as an education of less than or equal to 9 years (reference category), upper secondary education of 1 to 3 years, and post-secondary and post-graduate education. We coded maternal, paternal, and average parental income at childbirth as percentiles of 0 to 20 (reference), 20 to 40, 40 to 60, 60 to 80, and 80 to 100. A category of “missing” was included as a dummy code for all covariates when appropriate.

Statistical analyses

We estimated statistical associations using either logistic regression (for binary response variables) or Cox proportional hazards models (for right-censored variables). In the first set of analyses, we measured the associations between maternal and paternal SMI and offspring PTB, LBW, and SGA using three logistic regression models. The baseline model estimated the association between maternal and paternal SMI and each pregnancy outcome while controlling for offspring year of birth, sex, and parity. The SMI adjusted model accounted for the previous covariates, as well as SMI in the other parent. Finally, the adjusted model accounted for all measured covariates.

In the second set of analyses, we estimated hazard ratios (HR) for the associations between the pregnancy outcomes (PTB, LBW, and SGA) and offspring ASD and ADHD using two Cox regression models. The baseline model adjusted for offspring sex and parity and the adjusted model accounted for all covariates. We included all measures of adverse birth outcome in the models in order to capture multiple aspects of problematic fetal development.

Finally, four Cox regression models were used to predict offspring ASD and ADHD from parental SMI. In Model 1 we measured the association between maternal and paternal SMI and offspring ASD and ADHD while controlling for offspring sex and parity. Model 2, similar to the previously described SMI adjusted model, assessed the association while also adjusting for SMI in the other parent. Model 3 measured the association while controlling for all covariates. Finally, Model 4 measured the association between maternal and paternal SMI and offspring ASD and ADHD while adjusting for all covariates, as well as gestational age, birth weight, and SGA, to examine whether pregnancy outcomes mediate the association between parental SMI and offspring neurodevelopmental problems. If pregnancy-related problems mediate the association, we would expect that the association between parental SMI and offspring ASD and ADHD would be attenuated in Model 4 as compared with the other models.

RESULTS

Demographics

Table 1 provides descriptive statistics for all eligible offspring born between 1992 and 2001 in Sweden. Of these offspring, 7,236 (0.8%) have received a diagnosis of ASD, 15,254 (1.8%) have received a diagnosis of ADHD, 19,288 (2.2%) were born SGA, 25,748 (3.0%) were born LBW, and 37,451 (4.3%) were PTB. A total of 9,134 (1.5%) mothers and 8,285 (1.4%) fathers were diagnosed with a SMI.

Table 1.

Descriptive characteristics of offspring born in Sweden 1992-2001

Variable N % of Total Sample
Offspring Characteristics (N = 870,017)
 Sex
  Female 425,218 48.9%
  Malea 444,799 51.1%
 Parity
  Firsta 356,593 41.0%
  Second 322,893 37.1%
  Third 131,831 15.2%
  Fourth or higher 58,700 6.7%
 Maternal Age at Childbirth (years)
  under 21 17,941 2.1%
  21-24 150,268 17.3%
  25-29a 319,598 36.7%
  30-35 259,498 29.8%
  over 35 122,712 14.1%
 Paternal Age at Childbirth (years)
  under 21 4,967 0.6%
  21-24 73,633 8.5%
  25-29a 253,988 29.2%
  30-35 286,383 32.9%
  over 35 249,813 28.7%
  Missing 1,233 0.1%
 Maternal Disposable Income at Birth (percentile)
  0-20a 92,742 10.7%
  20-40 161,367 18.5%
  40-60 231,371 26.6%
  60-80 212,402 24.4%
  80-100 167,436 19.2%
  Missing 4,699 0.5%
 Paternal Disposable Income at Birth (percentile)
  0-20a 95,940 11.0%
  20-40 163,917 18.8%
  40-60 228,221 26.2%
  60-80 210,774 24.2%
  80-100 162,782 18.7%
  Missing 8,383 1.0%
 Parental Disposable Income at Birth (percentile)
  0-20a 96,797 11.1%
  20-40 163,430 18.8%
  40-60 222,190 25.5%
  60-80 207,746 23.9%
  80-100 176,622 20.3%
  Missing 3,232 0.4%
 Parental Cohabitation Status at Birth
  Cohabitationa 772,180 88.8%
  No Cohabitation 38,985 4.5%
  Missing 58,852 6.8%
 Gestational Age
  23 weeks to 27 weeks 6 days 1,016 0.1%
  28 weeks to 30 weeks 6 days 2,148 0.2%
  31 weeks to 33 weeks 6 days 5,685 0.7%
  34 weeks to 36 weeks 6 days 28,602 3.3%
  37 weeks to 42 weeks 6 daysa 832,556 95.7%
 Birth weight (grams)
  less than 2500 25,748 3.0%
  2500-3000 86,836 10.0%
  3000-3500 271,963 31.3%
  3500-4000a 311,723 35.8%
  greater than 4000 171,176 19.7%
  Missing BW 2,571 0.3%
 Small for Gestational Age
  Not SGAa 846,984 97.4%
  SGA 19,288 2.2%
  Missing 3,745 0.4%
 ASD
  No ASDa 862,781 99.2%
  ASD 7,236 0.8%
 ADHD
  No ADHDa 854,763 98.2%
  ADHD 15,254 1.8%

Maternal Characteristics (N=597,264)
 Country of Origin
  Sweden 505,249 84.6%
  Not Swedena 91,461 15.3%
  Missing 554 0.1%
 Highest Level of Education
  Less than 9 yearsa 56,985 9.5%
  Upper secondary education of 1-3 years 301,904 50.5%
  Post-secondary and post-graduate education 236,614 39.6%
  Missing 1,761 0.3%
 Criminality
  No Criminalitya 523,772 87.7%
  Criminality 73,492 12.3%
 Severe Mental Illness
  No Severe Mental Illnessa 588,130 98.5%
  Severe Mental Illness 9,134 1.5%

Paternal Characteristics (N=599,747)
 Country of Origin
  Swedena 503,514 84.1%
  Not Sweden 95,388 15.9%
  Missing 845 0.1%
 Highest Level of Education
  Less than 9 yearsa 86,555 14.4%
  Upper secondary education of 1-3 years 323,004 53.9%
  Post-secondary and post-graduate education 187,729 31.3%
  Missing 2,459 0.4%
 Criminality
  No Criminalitya 344,971 57.5%
  Criminality 254,776 42.5%
 Severe Mental Illness
  No Severe Mental Illnessa 591,462 98.6%
  Severe Mental Illness 8,285 1.4%
a

Reference

Parental SMI and adverse pregnancy outcomes

Table 2 provides the results of logistic regression analyses for maternal and paternal SMI predicting PTB, LBW, and SGA. Even in the adjusted models, both maternal and paternal SMI were associated with an increased risk for PTB (adjusted OR (aOR)maternal=1.26; 95% CI=1.16-1.37; aORpaternal = 1.10; 95% CI=1.01-1.21) and SGA (aORmaternal=1.13; 95% CI=1.01-1.26; aORpaternal = 1.13; 95% CI=1.01-1.28). With respect to LBW, maternal SMI continued to predict an increased risk (aORmaternal=1.31; 95% CI=1.19-1.45), while associations between paternal SMI and LBW were attenuated in the adjusted model (aORpaternal=1.11; 95% CI=0.99-1.23). Similar results were obtained when using parental SMI as a combined measure of maternal and paternal SMI, such that parental SMI was associated with an increased risk of each outcome (results available upon request).

Table 2.

Results of parental severe mental illness predicting pregnancy outcomes

BASELINE SMI ADJUSTED ADJUSTED
Outcomes OR 95% CI OR 95% CI OR 95% CI
Preterm Birth
 - Maternal SMI 1.39* 1.29-1.50 1.38* 1.28-1.49 1.26* 1.16-1.37
 - Paternal SMI 1.21* 1.11-1.32 1.20* 1.10-1.30 1.10* 1.01-1.21
Low Birth Weight
 - Maternal SMI 1.52* 1.39-1.66 1.51* 1.38-1.65 1.31* 1.19-1.45
 - Paternal SMI 1.28* 1.16-1.41 1.26* 1.14-1.39 1.11 0.99-1.23
Small for Gestational Age
 - Maternal SMI 1.39* 1.25-1.54 1.37* 1.23-1.53 1.13* 1.01-1.26
 - Paternal SMI 1.37* 1.22-1.53 1.35* 1.21-1.51 1.13* 1.01-1.28

Note: SMI= severe mental illness, OR = odds ratio, CI = confidence interval,

*

p<0.05. The baseline model controlled for offspring year of birth, sex, and parity. The SMI adjusted model additionally controlled for SMI in the other parent. The adjusted model additionally controlled for all measured covariates.

Adverse pregnancy outcomes and offspring neurodevelopmental problems

Table 3 presents the results of Cox regression analyses predicting ASD and ADHD from PTB, LBW, and SGA. Each birth outcome was associated with an increased risk of ASD that was independent of measured covariates (e.g., adjusted HR (aHR)LBW=1.79; CI=1.60-2.00). PTB, LBW, and SGA were also predictive of similar magnitudes of increased risk for ADHD (e.g., aHRLBW=1.73; CI=1.61-1.87). Comparable results were obtained when using ordinal outcomes of gestational age and birth weight, presented in Table A1 online.

Table 3.

Results of pregnancy outcomes predicting ASD and ADHD

Predictors BASELINE ADJUSTED
HR 95% CI HR 95% CI
ASD
 Preterm Birth 1.53* 1.40-1.67 1.47* 1.34-1.62
 Low Birth Weighta 1.92* 1.74-2.13 1.79* 1.60-2.00
 Small for Gestational Agea 1.69* 1.50-1.91 1.50* 1.32-1.71
ADHD
 Preterm Birth 1.53* 1.43-1.62 1.42* 1.31-1.51
 Low Birth Weighta 1.90* 1.77-2.04 1.73* 1.61-1.87
 Small for Gestational Agea 1.71* 1.57-1.86 1.58* 1.45-1.73

Note: None of the cases missing birth weight or SGA had a diagnosis of ASD or ADHD, so missing birth weight and SGA were dropped from the analyses; ASD = Autism Spectrum Disorder, ADHD = Attention Deficit Hyperactivity Disorder, HR = hazard ratio, CI = confidence interval,

*

p<0.05. The baseline model controlled for offspring sex and parity. The adjusted model additionally controlled for all measured covariates.

Parental SMI and offspring neurodevelopmental problems

Table 4 provides the results of models 1-4 predicting ASD and ADHD from parental SMI. In Models 1 and 2, maternal and paternal SMI were associated with an increased risk of offspring ASD and ADHD. These associations were attenuated, but remained after controlling for all covariates in Model 3 for both mothers (aHRASD=1.73; 95% CI=1.50-1.99; aHRADHD=1.59; 95% CI=1.44-1.79) and fathers (aHRASD=1.59, 95% CI=1.37-1.85; aHRADHD=1.47; 95% CI=1.32-1.62). The increased risk of offspring ASD and ADHD remained largely unchanged after controlling for birth outcomes in addition to all covariates in Model 4 for mothers (HRASD=1.72, 95% CI=1.46-2.02; HRADHD=1.56, 95% CI=1.39-1.74) and fathers (HRASD=1.56, 95% CI=1.31-1.86; HRADHD=1.36; 95% CI=1.21-1.54). Again, similar results were obtained when using a combined measure of parental SMI, such that parental SMI was associated with offspring ASD and ADHD and adverse pregnancy outcomes did not mediate the association.

Table 4.

Results of models 1-4 of maternal and paternal SMI predicting ASD and ADHD

Predictors Model 1 Model 2 Model 3 Model 4
HR 95% CI HR 95% CI HR 95% CI HR 95% CI
ASD
 Maternal SMI 2.04* 1.78-2.34 1.99* 1.74-2.28 1.73* 1.50-1.99 1.72* 1.46-2.02
 Paternal SMI 1.98* 1.72-2.28 1.92* 1.66-2.21 1.59* 1.37-1.85 1.56* 1.31-1.86
ADHD
 Maternal SMI 2.16* 1.97-2.37 2.09* 1.90-2.29 1.59* 1.44-1.75 1.56* 1.39-1.74
 Paternal SMI 2.21* 2.01-2.43 2.13* 1.94-2.35 1.47* 1.32-1.62 1.36* 1.21-1.54

Note: SMI= severe mental illness, ASD = Autism Spectrum Disorder, ADHD = Attention Deficit Hyperactivity Disorder, HR = hazard ratio, CI = confidence interval,

*

p<0.05. Model 1 controlled for offspring sex and parity. Model 2 additionally controlled for SMI in the other parent. Model 3 additionally controlled for all measured covariates. Model 4 additionally controlled for gestational age, birth weight, and small for gestational age.

Sensitivity Analyses

Parental SMI variables were limited to instances where the parent received the SMI diagnosis prior to childbirth in order to clarify the directionality of association. This limited the exposures to (XXX) maternal SMI and (XXX) paternal SMI instances. With this more restrictive definition or parental SMI and while adjusting for the other parents’ SMI, maternal (aORmaternal=1.13; 95% CI=1.01-1.26) and paternal (aORpaternal = 1.10; 95% CI=1.01-1.21) SMI continued to predict offspring ASD. Maternal (aORmaternal=1.13; 95% CI=1.01-1.26) and paternal (aORpaternal = 1.10; 95% CI=1.01-1.21) SMI also continued to predict offspring ADHD in the adjusted model. When testing for mediation by adverse birth outcome, increased risk of offspring ASD and ADHD was robust after controlling for birth outcomes in addition to all covariates for mothers (HRASD=1.72, 95% CI=1.46-2.02; HRADHD=1.56, 95% CI=1.39-1.74) and fathers (HRASD=1.56, 95% CI=1.31-1.86; HRADHD=1.36; 95% CI=1.21-1.54).

DISCUSSION

The current study utilized a large, Swedish population-based sample to examine the associations between parental SMI, adverse pregnancy outcomes, and offspring neurodevelopmental problems. The results suggest that adverse pregnancy outcomes do not mediate the association between maternal and paternal SMI and offspring ASD and ADHD. More specifically, in agreement with previous research [15-18], we found that the risk of offspring PTB, LBW, and SGA are elevated if the mother and/or father has a SMI. With the exception of the association between paternal SMI and offspring LBW, these associations remained robust in the adjusted models. Although not statistically different, the variation in magnitude of association between mothers and fathers suggests that future research may benefit from continuing to explore if the relation between maternal SMI and pregnancy outcomes is partially influenced by the prenatal environment [15, 19, 32].

We also identified associations between PTB, LBW, and SGA and offspring ASD and ADHD that were independent of measured covariates. These findings are largely consistent with other studies assessing adverse pregnancy outcomes as risk factors for ASD and ADHD [20-27, 45]. A recent sibling-comparison study has supported the independent association between PTB and offspring ASD [28], and researchers have suggested that abnormalities in the brain development of offspring born preterm are associated with an increased risk of ADHD [46]. LBW and SGA also are associated with many diseases later in life, including mental illness, which may be the result of fetal growth restriction [29, 30]. Thus, the association between adverse pregnancy outcomes and neurodevelopmental problems found in this study may be explained by similar mechanisms. For example, previous research has found that LBW is associated with white matter abnormalities and cortical surface area and brain volume shows variation even across normal birth weights [47, 48]. Poor maternal nutrition during pregnancy may also be contributing to fetal growth and altered brain development [49].

In addition, our results suggest that offspring of parents with a SMI are at an increased risk of ASD and ADHD. These associations were largely unchanged after controlling for birth outcomes, suggesting that adverse pregnancy outcomes do not mediate the association between parental SMI and offspring neurodevelopmental problems. This finding supports those of a smaller study that found parental SMI and adverse pregnancy outcomes to be independent risk factors for ASD [10]. The current study extends this previous research by showing that maternal and paternal SMI are independent risk factors for ASD [10] and shows a novel parallel association with offspring ADHD, a highly related outcome [34]. However, future research should explore the association between paternal SMI, ADHD, and possible mediation by adverse birth outcomes because unlike the other associations, this association was slightly reduced in magnitude with the additional control of adverse birth outcomes (HR of 1.47 vs. 1.36). Environmental or genetic vulnerability may be responsible for partial mediation, if found in future research.

The similarity in magnitudes of the increased risk of offspring neurodevelopmental problems for maternal and paternal SMI suggests that observed associations are likely due to genetic and/or environmental factors rather than causal intrauterine effects [32]. If the association were due to intrauterine effects, maternal SMI would have showed a stronger association with offspring ASD than with paternal SMI, thus mental illnesses may share common etiological factors, such as genetic risk [8, 11, 14, 34].

Limitations

Despite using a large, population-based sample with well-validated measures and controlling for several offspring and parental characteristics, our findings must be interpreted in light of a number of limitations. First, Swedish health care and diagnostic practices may differ from other countries. Second, our definition of parental SMI required that individuals be diagnosed in an inpatient setting, which may lead to over or underestimation of the magnitude of associations. Future research is needed to examine the studied relations using parental SMI prior to childbirth, indexed at disease onset, and including outpatient diagnoses. Additionally, including other measures of neurodevelopmental disorders will help to determine the specificity of our findings. Finally, while PTB, LBW, and SGA are important indices, they are merely proxies for restricted fetal growth and brain development. We included all three measures of adverse birth outcome to capture the most complete measure of problematic fetal development. However, fetal insults that are not indexed by PTB, LBW, or SGA may still mediate the association between parental SMI and offspring neurodevelopmental problems.

Conclusion

Adverse pregnancy outcomes and maternal and paternal SMI are independent risk factors for ASD and ADHD. The similarity in moderate association magnitude between maternal and paternal SMI and offspring ASD and ADHD indicates that the associations are likely not due to causal intrauterine effects [32]. Our results provide evidence consistent with the theory that genetic factors may explain much of the association between parental SMI and offspring ASD and ADHD [14]. Future research should continue to focus on elucidating the mechanisms underlying the independent associations between parental SMI, adverse pregnancy outcomes, and offspring ASD and ADHD, as this could provide etiological information about these neurodevelopmental problems.

Supplementary Material

Supplementary Appendix

ACKNOWLEDGEMENTS

None.

FUNDING

The study was supported by grants from the National Institute of Child Health and Development (HD061817), National Institute of Mental Health (MH094011), the Swedish Council for Working Life and Social Research, the Swedish Research Council (Medicine), and the Swedish Society of Medicine.

List of Abbreviations

SMI

Parental severe mental illness

ASD

Autism Spectrum Disorder

ADHD

Attention Deficit Hyperactivity Disorder

PTB

preterm birth

LBW

low birth weight

SGA

small for gestational age

Footnotes

COMPETING INTERESTS

None.

REFERENCES

  • [1].Whitehouse AJO, Hickey M, Ronald A. Are autistic traits in the general population stable across development? PLoS One. 2011;6(8):e23029. doi: 10.1371/journal.pone.0023029. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [2].Ganz ML. The lifetime distribution of the incremental societal costs of autism. Arch Pediatr Adolesc Med. 2007;161(4):343–9. doi: 10.1001/archpedi.161.4.343. [DOI] [PubMed] [Google Scholar]
  • [3].Lecavalier L, Leone S, Wiltz J. The impact of behaviour problems on caregiver stress in young people with autism spectrum disorders. J Intell Disabil Res. 2006;50:172–83. doi: 10.1111/j.1365-2788.2005.00732.x. [DOI] [PubMed] [Google Scholar]
  • [4].Pelham WE, Foster EM, Robb JA. The economic impact of attention-deficit/hyperactivity disorder in children and adolescents. J Pediatr Psychol. 2007;32(6):711–27. doi: 10.1093/jpepsy/jsm022. Epub June 7, 2007. [DOI] [PubMed] [Google Scholar]
  • [5].Theule J, Wiener J, Tannock R, Jenkins JM. Parenting stress in families of children with ADHD: a meta-analysis. J Emot Behav Disord. 2013;21(1):3–17. [Google Scholar]
  • [6].Autism and Developmental Disabilities Monitoring Network Surveillance Year 2008 Principal Investigators and Centers for Disease Control and Prevention Prevalence of autism spectrum disorders – autism and developmental disabilities monitoring network, 14 sites, United States, 2008. MMWR Surveill Summ. 2012;61(3):1–19. Epub 2012/03/30. [PubMed] [Google Scholar]
  • [7].CDC Increasing prevalence of parent-reported Attention-Deficit/Hyperactivity Disorder among children --- United States, 2003 and 2007. MMWR. 2010;59(44):1439–43. [PubMed] [Google Scholar]
  • [8].Sullivan PF, Magnusson C, Reichenberg A, Boman M, Dalman C, Davidson M, et al. Family history of schizophrenia and bipolar disorder as risk factors for autism. Arch Gen Psychiatry. 2012;69(11):1099–103. doi: 10.1001/archgenpsychiatry.2012.730. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [9].Daniels JL, Forssen U, Hultman CM, Cnattingius S, Savitz DA, Feychting M, et al. Parental psychiatric disorders associated with autism spectrum disorders in the offspring. Pediatrics. 2008;121(5):E1357–E62. doi: 10.1542/peds.2007-2296. [DOI] [PubMed] [Google Scholar]
  • [10].Larsson HJ, Eaton WW, Madsen KM, Vestergaard M, Olesen AV, Agerbo E, et al. Risk factors for autism: perinatal factors, parental psychiatric history, and socioeconomic status. Am J Epidemiol. 2005;161(10):916–25. doi: 10.1093/aje/kwi123. [DOI] [PubMed] [Google Scholar]
  • [11].Larsson H, Rydén E, Boman M, Långström N, Lichtenstein P, Landén M. Risk of bipolar disorder and schizophrenia in relatives of people with attention-deficit hyperactivity disorder. Br J Psychiatry. 2013 doi: 10.1192/bjp.bp.112.120808. Epub May 23, 2013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [12].Lichtenstein P, Yip BH, Bjork C, Pawitan Y, Cannon TD, Sullivan PF, et al. Common genetic determinants of schizophrenia and bipolar disorder in Swedish families: a population-based study. Lancet. 2009;373(9659):234–9. doi: 10.1016/S0140-6736(09)60072-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [13].Lichtenstein P, Carlstrom E, Rastam M, Gillberg C, Anckarsater H. The genetics of autism spectrum disorders and related neuropsychiatric disorders in childhood. Am J Psychiat. 2010;167(11):1357–63. doi: 10.1176/appi.ajp.2010.10020223. [DOI] [PubMed] [Google Scholar]
  • [14].Smoller JW, Craddock N, Kendler K, Lee PH, Neale BM, Nurnberger JI, et al. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet. 2013;381(9875):1371–9. doi: 10.1016/S0140-6736(12)62129-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [15].MacCabe JH, Martinsson L, Lichtenstein P, Nilsson E, Cnattingius S, Murray RM, et al. Adverse pregnancy outcomes in mothers with affective psychosis. Bipolar Disord. 2007;9(3):305–9. doi: 10.1111/j.1399-5618.2007.00382.x. [DOI] [PubMed] [Google Scholar]
  • [16].Nilsson E, Hultman CM, Cnattingius S, Olausson PO, Bjork C, Lichtenstein P. Schizophrenia and offspring’s risk for adverse pregnancy outcomes and infant death. Br J Psychiatry. 2008;193(4):311–5. doi: 10.1192/bjp.bp.107.045146. [DOI] [PubMed] [Google Scholar]
  • [17].Nilsson E, Lichtenstein P, Cnattingius S, Murray RM, Hultman CM. Women with schizophrenia: pregnancy outcome and infant death among their offspring. Schizophr Res. 2002;58(2-3):221–9. doi: 10.1016/s0920-9964(01)00370-x. [DOI] [PubMed] [Google Scholar]
  • [18].Lee HC, Lin HC. Maternal bipolar disorder increased low birthweight and preterm births: a nationwide population-based study. J Affect Disord. 2010;121(1-2):100–5. doi: 10.1016/j.jad.2009.05.019. [DOI] [PubMed] [Google Scholar]
  • [19].Bennedsen BE. Adverse pregnancy outcome in schizophrenic women: occurrence and risk factors. Schizophr Res. 1998;33(1-2):1–26. doi: 10.1016/s0920-9964(98)00065-6. [DOI] [PubMed] [Google Scholar]
  • [20].Hack M, Taylor HG, Schluchter M, Andreias L, Drotar D, Klein N. Behavioral outcomes of extremely low birth weight children at age 8 years. J Dev Behav Pediatr. 2009;30(2):122–30. doi: 10.1097/DBP.0b013e31819e6a16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [21].Schendel D, Bhasin TK. Birth weight and gestational age characteristics of children with autism, including a comparison with other developmental disabilities. Pediatrics. 2008;121(6):1155–64. doi: 10.1542/peds.2007-1049. [DOI] [PubMed] [Google Scholar]
  • [22].Guinchat V, Thorsen P, Laurent C, Cans C, Bodeau N, Cohen D. Pre-, peri- and neonatal risk factors for autism. Acta Obstet Gynecol Scand. 2012;91(3):287–300. doi: 10.1111/j.1600-0412.2011.01325.x. [DOI] [PubMed] [Google Scholar]
  • [23].Moore GS, Kneitel AW, Walker CK, Gilbert WM, Xing GB. Autism risk in small- and large-for-gestational-age infants. Am J Obstet Gynecol. 2012;206(4):314. doi: 10.1016/j.ajog.2012.01.044. e1-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [24].Moster D, Lie RT, Markestad T. Long-term medical and social consequences of preterm birth. N Engl J Med. 2008;359(3):262–73. doi: 10.1056/NEJMoa0706475. [DOI] [PubMed] [Google Scholar]
  • [25].Lindstrom K, Lindblad F, Hjern A. Preterm birth and attention-deficit/hyperactivity disorder in schoolchildren. Pediatrics. 2011;127(5):858–65. doi: 10.1542/peds.2010-1279. [DOI] [PubMed] [Google Scholar]
  • [26].Nigg JT, Breslau N. Prenatal smoking exposure, low birth weight, and disruptive behavior disorders. J Am Acad Child Adolesc Psychiatr. 2007;46(3):362–9. doi: 10.1097/01.chi.0000246054.76167.44. [DOI] [PubMed] [Google Scholar]
  • [27].Halmoy A, Klungsoyr K, Skjaerven R, Haavik J. Pre- and perinatal risk factors in adults with attention-deficit/hyperactivity disorder. Biological Psychiatry. 2012;71(5):474–81. doi: 10.1016/j.biopsych.2011.11.013. Epub 2011/12/28. [DOI] [PubMed] [Google Scholar]
  • [28].D’Onofrio BM, Class QA, Rickert ME, Larsson H, Langstrom N, Lichtenstein P. Preterm birth and mortality and morbidity: a population-based quasi-experimental study. JAMA Psychiatry. 2013 doi: 10.1001/jamapsychiatry.2013.2107. Epub September 25, 2013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [29].Bale TL, Baram TZ, Brown AS, Goldstein JM, Insel TR, McCarthy MM, et al. Early life programming and neurodevelopmental disorders. Biol Psychiatry. 2010;68(4):314–9. doi: 10.1016/j.biopsych.2010.05.028. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [30].Schlotz W, Phillips DIW. Fetal origins of mental health: evidence and mechanisms. Brain Behav Immun. 2009;23(7):905–16. doi: 10.1016/j.bbi.2009.02.001. [DOI] [PubMed] [Google Scholar]
  • [31].Baron RM, Kenny DA. The moderator mediator variable distinction in social psychological research - conceptual, strategic, and statistical considerations. J Pers Soc Psychol. 1986;51(6):1173–82. doi: 10.1037//0022-3514.51.6.1173. [DOI] [PubMed] [Google Scholar]
  • [32].Smith GD. Assessing intrauterine influences on offspring health outcomes: can epidemiological studies yield robust findings? Basic Clin Pharmacol Toxicol. 2008;102(2):245–56. doi: 10.1111/j.1742-7843.2007.00191.x. [DOI] [PubMed] [Google Scholar]
  • [33].Simonoff E, Pickles A, Charman T, Chandler S, Loucas T, Baird G. Psychiatric disorders in children with autism spectrum disorders: prevalence, comorbidity, and associated factors in a population-derived sample. Journal of American Academy for child and adolescent psychiatry. 2008;47:921–9. doi: 10.1097/CHI.0b013e318179964f. [DOI] [PubMed] [Google Scholar]
  • [34].Pettersson E, Anckarsater H, Gillberg C, Lichtenstein P. Different neurodevelopmental symptoms have a common genetic etiology. Journal of Child Psychology & Psychiatry. 2013;54:1356–65. doi: 10.1111/jcpp.12113. [DOI] [PubMed] [Google Scholar]
  • [35].Statistics Sweden . Multi-generation register 2005 – A description of contents and quality. Vol. 2006. Statistics Sweden; Örebro: 2006. p. 6. [Google Scholar]
  • [36].Centre for Epidemiology The Swedish Medical Birth Register - A Summary of Content and Quality. 2003 [Google Scholar]
  • [37].Killeya-Jones LA, Costanzo PR, Malone P, Quinlan NP, Miller-Johnson S. Norm-narrowing and self- and other-perceived aggression in early-adolescent same-sex and mixed-sex cliques. Journal of School Psychology. 2007;45:549–65. doi: 10.1016/j.jsp.2007.04.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [38].Mathews TJ, MacDorman MF. Infant mortality statistics from the 2005 period linked birth/infant death data set. Natl Vital Stat Rep. 2008;57(2):1–32. [PubMed] [Google Scholar]
  • [39].Ekholm B, Ekholm A, Adolfsson R, Vares M, Osby U, Sedvall GC, et al. Evaluation of diagnostic procedures in Swedish patients with schizophrenia and related psychoses. Nord J Psychiatr. 2005;59(6):457–64. doi: 10.1080/08039480500360906. [DOI] [PubMed] [Google Scholar]
  • [40].Idring S, Rai D, Dal H, Dalman C, Sturm H, Zander E, et al. Autism spectrum disorders in the Stockholm youth cohort: design, prevalence and validity. PLoS One. 2012;7(7):e41280. doi: 10.1371/journal.pone.0041280. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [41].Larsson H, Rydén E, Boman M, Långström N, Lichtenstein P, Landén M. Risk of bipolar disorder and schizophrenia in relatives of people with attention-deficit hyperactivity disorder. The British Journal of Psychiatry. 2013 doi: 10.1192/bjp.bp.112.120808. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [42].Sullivan PF, Magnusson C, Reichenberg A, Boman M, Dalman C, Davidson M, et al. Family History of Schizophrenia and Bipolar Disorder as Risk Factors for AutismFamily History of Psychosis as Risk Factor for ASD. Arch Gen Psychiatry. 2012:1–5. doi: 10.1001/archgenpsychiatry.2012.730. Epub 2012/07/04. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [43].Idring S, Rai D, Dal H, Dalman C, Sturm H, Zander E, et al. Autism Spectrum Disorders in the Stockholm Youth Cohort: Design, Prevalence and Validity. PLOS One. 2012;7:e41280. doi: 10.1371/journal.pone.0041280. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [44].Fazel S, Grann M. The population impact of severe mental illness on violent crime. Am J Psychiat. 2006;163(8):1397–403. doi: 10.1176/ajp.2006.163.8.1397. [DOI] [PubMed] [Google Scholar]
  • [45].D’Onofrio BM, Class QA, Rickert ME, Larsson H, Långström N, Lichtenstein P. Preterm birth and mortality and morbidity: a population-based quasi-experimental study. JAMA Psychiatry in press. doi: 10.1001/jamapsychiatry.2013.2107. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [46].Whitaker AH, Feldman JF, Lorenz JM, McNicholas F, Fisher PW, Shen S, et al. Neonatal head ultrasound abnormalities in preterm infants and adolescent psychiatric disorders. Arch Gen Psychiatry. 2011;68(7):742–52. doi: 10.1001/archgenpsychiatry.2011.62. [DOI] [PubMed] [Google Scholar]
  • [47].Walhovd KB, Fjell AM, Brown TT, Kuperman JM, Chung Y, Hagler DJ, Jr., et al. Long-term influence of normal variation in neonatal characteristics on human brain development. Proceedings of the National Academy of Sciences. 2012;109(49):20089–94. doi: 10.1073/pnas.1208180109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [48].Skranes J, Evensen KI, Lohaugen GC, Martinussen M, Kulseng M, Myhr G, et al. Abnormal cerebral MRI findings and neuroimpairments in very low birth weight (VLBW) adolescents. European Journal of Paediatric Neurology. 2009;12:273–83. doi: 10.1016/j.ejpn.2007.08.008. [DOI] [PubMed] [Google Scholar]
  • [49].de Bie HM, Oostrom KJ, Delemarre-van de Waal HA. Brain development, intelligence and cognitive outcome in children born small for gestational age. Paediatrics. 2010;73:6–14. doi: 10.1159/000271911. [DOI] [PubMed] [Google Scholar]

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